Matrix attachment regions (MARs) are essential regulatory DNA elements of
eukaryotic cells. They are major determinants of locus control of expression
and shield gene expression from position effects. Experimental detection of
MARs requires substantial efforts not suitable for large-scale screening of
genomic sequences. In silico prediction of MARs can provide a crucial first
selection step to reduce the amount of candidates. We used 34 experimentally
defined MARs as training set and generated a library of 97 MAR-associated, AT-rich
patterns described as weight matrices. We developed a new tool, SMARTest, identifying
potential MARs in genomic sequences. SMARTest carries out a density analysis
based on the MAR matrix library. The SMARTest approach does not depend on the
sequence context and is suitable to analyse long genomic sequences up to the
size of whole chromosomes on a workstation. To demonstrate the feasibility of
large-scale MAR prediction we analysed the recently published chromosome 22
sequence and found 1198 MAR candidates.